Precocious Pregnancy,Sexual Conflict,and Early Childbearing in Remote Aboriginal Australia

Ideas from evolutionary theory and a consideration of social and cultural factors are used to argue that teenage pregnancy in three remote Aboriginal communities represents a strategic response to current environments characterised by pervasive and sustained risk and uncertainty. Ethnographic studies of the communities find that these environments both provoke and enable the reproductive strategies of adolescent boys and girls but raise the question of the effects of father absent socialisation.     

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.     

Sex,gender, and difference

Empirical research has demonstrated that women’s aggressive behavior is widespread and displays regularities across societies. Until recently, however, discussions about the aggressive behavior of women and gender differences in aggressive behavior have been based largely on data from nonhuman primates, children, or laboratory experiments. Using a unique corpus of naturalistic data on aggressive human interactions both between and among men and women, I explore the complexity of our questions about sex differences in aggression and further illuminate the ways in which men and women may use aggression in human interactions. In this paper I compare the aggressive behavior of men and women in an Australian Aboriginal community. In doing so I argue for the continuing use of a “sex differences” framework for organizing our understanding of gender relations and gender hierarchy. I believe, however, that this form of analysis benefits from, if not requires, a sensitivity to the most taken-for-granted aspects of our gender ideology and a commitment to attend to evidence that challenges our convictions about men and women.